Mental Flow Estimation Through Wearable EEG

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Small scale ultrasonic piezoelectric actuators performance strongly depends on not well-known contact dynamics. Deep Neural Network (DNN) sees their use in physic simulation growing as their flexibility allows better performance especially when dynamics la ...

Proprioception tells the brain the state of the body based on distributed sensory neurons. Yet, the principles that govern proprioceptive processing are poorly understood. Here, we employ a task -driven modeling approach to investigate the neural code of p ...

In our study, we investigated the impact of changes in Mode I fracture toughness and stress barriers on fully developed planar, buoyant hydraulic fractures assuming linear elastic hydraulic fracture mechanics. We present scaling-based arguments to predict ...

Deep neural networks may easily memorize noisy labels present in real-world data, which degrades their ability to generalize. It is therefore important to track and evaluate the robustness of models against noisy label memorization. We propose a metric, ca ...

Deep spiking neural networks (SNNs) offer the promise of low-power artificial intelligence. However, training deep SNNs from scratch or converting deep artificial neural networks to SNNs without loss of performance has been a challenge. Here we propose an ...

Mental Flow Estimation Through Wearable EEG

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Normal Contact Force Estimation Using Deep Learning

Task-driven neural network models predict neural dynamics of proprioception

How Stress Barriers and Fracture Toughness Heterogeneities Arrest Buoyant Hydraulic Fractures

Leveraging Unlabeled Data to Track Memorization

An exact mapping from ReLU networks to spiking neural networks